Field
The invention relates in general to the field of characterization of a sample by spectrometry. In particular the invention relates to a device and a method to determine a corrected spectrum of a sample from an initial measured spectrum of the sample performed through a translucent material such as a transparent packaging. The corrected spectrum is then able to be introduced into a characterization model to perform classification or quantification operations on the sample.
Related Art
In the frame of spectroscopy, and more particularly infrared (IR) spectroscopy, the optical properties of a sample are determined by measuring the intensity I0 incident on the sample S, and the intensity I transmitted or reflected by the sample, for a plurality of wavelengths inside a specific range [λ1; λ2], as shown on FIG. 1 in the case of a reflective sample S. The interaction between the light and the sample permits the characterization of the sample.
The different wavelengths are generated by a light source LS, and the reflected (or transmitted) intensity is measured on a detector D. A processing unit PU calculates the spectrum Ss(λ) corresponding to a signal, dependant on λ, determined from the ratio between I and I0 or its inverse.
The term spectrum describes different types of signals, for example:
In transmission the transmittance of the sample is defined as: T(λ)=It(λ)/I0(λ) where It is the transmitted intensity
In reflection the reflectance of the sample is defined as R(λ)=IR(λ)/I0(λ), where IR is the reflected intensity
The reflection opacity OR(λ) is defined as 1/R, and the transmission opacity Ot(λ) is defined as 1/T.
The absorbance As(λ) is defined as:For reflection: As/R(λ)=log10 [I0(λ)/IR(λ)]For transmission: As/t(λ)=log10 [I0(λ)/It(λ)]  (1)
The physical quantities defined as:For reflection: As/R′(λ)=log10 [IR(λ)/I0(λ)]For transmission: As/t′(λ)=log10 [It(λ)/I0(λ)]  (2)can also be used as a spectrum.
The absorbance log10 [I0(λ)/I(λ)] or log10 [I(λ)/I0(λ)] are used in spectroscopy, both in transmissive (I(λ)=It(λ)) or reflective (I(λ)=IR(λ)) configurations with the benefit that multiplicative relationships are transformed into additive or subtractive relationships.
The measured spectrum Ss(λ) is then used as an input into a characterization model CM in order for example to classify the sample or to quantify a particular compound of the sample.
An example of classification is the determination of the category of a flour sample among a plurality of predetermined categories of flours. Examples of quantification are: humidity level in a flour, quantification of gluten in a flour, percentage of cotton in a fabric . . . . Another possibility is to perform a classification of the sample by determining if a compound present in the sample is below or above a threshold. All these types of characterization models are commonly defined as classification/quantification models.
The characterization models, well known in the art, are based on a reference database DB of a substantial number of measured spectra of reference samples, of the different categories (classification) or having a different percentage of the compound to quantify (quantification).
The reference spectra of the database are used to calibrate the model, based for example on well known model such as partial least square discrimination (PLS-DA), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA) or k Nearest Neighbours (kNN).
The models are trained using both reference spectra of the database and associated information related to the known class of the sample. For each type of model, different criteria are optimized in order to estimate the statistical properties of each class.
Once calibrated, the developed model CM is capable of extracting searched information such as class, quantified parameter . . . on the basis of an unknown spectrum, used as input, as illustrated on FIG. 2.
Some preprocessing can be applied to the raw spectrum before being injected into the model, such as moving average smoothing, to improve the signal to noise ratio in order to reduce the effect caused by the variability of samples.
A limitation is that the characterization model is only able to determine the searched information from a spectrum of a sample which is similar to the reference spectra of the database, that is to say that the measurement of the spectrum of the sample may be performed in a manner as close as possible to that used to measure the reference spectra of the reference samples.
In some practical cases it is necessary to perform the measurement through a transparent or translucent material, such as a packaging or a window. This may be the case for example for food, fabrics, or any kind of industrial product once packaged. Such packaging typically comprises plastic materials, such as polyethylene (PE), polypropylene (PP), polyethylene terephtalate (PET).
In view of the chemical nature of these materials, their impact on the light illuminating the sample is not negligible, due to absorption, reflection and diffusion.
FIG. 3 illustrates the influence of a PP packaging on the absorbance of a coconut flour (reflective type sample) in the IR spectrum. FIG. 3a shows the measured absorbance of the flour alone As(λ), FIG. 3b shows the measured absorbance of the flour through the packaging As+p(λ), and FIG. 3c the measured absorbance of the packaging alone AP(λ). The packaging alone has been measured the same way as the flour, by replacing the flour by a material having a uniform reflectivity across the IR spectrum. Each spectrum comprises an average of 30 measurements.
The measurements of As, Ap and As+p have been performed with the same protocol in the same conditions. It can be seen that the measured spectrum is modified by the packaging.
For example the inventors have developed a model of classification of eight types of flours. 15 measurements of each type of flour have been performed with the flour alone that is to say in the absence of packaging material, to generate the database for the classification model (120 measurements). Based on the measurements of the database, a classification model was built, capable of identifying any sample flour of one the eight types from the measured absorbance of the sample flour alone. In this particular case, the classification model was developed using a kNN type model.
Then the measured spectrum of the sample flour through four different kinds of packaging is submitted to the model.
Two physically different packaging types, composed of polyethylene (PE), are respectively named PE1 and PE2
Two physically different packaging types, composed pf polyethylene terephtalate (PET), are respectively named PET1 and PET2.
The error rate of the model becomes:
PE1: 75%
PE2: 61%
PET1: 73%
PET2: 78%
The modification of the spectrum induced by the packaging leads to a highly increased error rate when the spectrum is applied to the classification model.
The publication “Influence of packaging in the analysis of fresh cut Valerianella locusta L. and golden delicious apple slices by visible-near IR and near-IR spectroscopy”, R. Beghi et al, Journal of Food Engineering 171 (2016), studies the influence of plastic packaging in the analysis of the freshness of apples and leaves. The paper evaluates the effect in terms of model performance. The authors explain that the packaging has a more important effect in the near IR than in the visible range, partly because of an increased absorption in this wavelength range.
A first classification model is built from a database of spectra of apples without packaging, and a second model is built from a database of spectra of apples with packaging. The performances of the two models are compared, but this publication does not try to explain nor suppress the packaging effect.
It is thus needed for an improved device and method for a robust characterization of a sample (classification/quantification) when the optical measurement leading to the characterization is performed through a translucent material disturbing the optical measurement.